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One of the main renewable energy sources for the future is photovoltaic (PV) energy. Hence, working of the PV systems at maximum efficiency is taken into consideration in recent years. In this paper, for improving the performance of the global maximum power point tracking under partial shading conditions and uncertainty in parameters of DC-DC converter, a two-level adaptive control scheme is proposed. The proposed controller is capable of efficiently handling the uncertainties in the PV systems and the perturbations in the environment. The first level is global perturbation-based extremum seeking control (GPESC), and the second level is model reference adaptive control (MRAC). GPESC is used to find global maximum power point and MRAC is utilized to handle the dynamics of the DC-DC converter. Adequate difference in the time constants of control levels, causes decoupled control levels, which in turn makes it easy to design the controller. The performance of the proposed control scheme is evaluated through simulation based on four indicators: tracking accuracy, tracking efficiency, tracking speed and searching resolution for different irradiance patterns. The results are compared with GPESC and GPESC with PID controller.

Nowadays, fuel cells (FCs) are considered suitable alternative sources for electrical energy applications. One major challenge encountered in FCs is relevant to the performance of the maximum power point tracking (MPPT) under FC parameter changes and load variations. This challenge is due to the nonlinearity and time-varying dynamics of FC systems. In this paper, the MPPT is studied in a system composed of a FC and a DC-DC converter. To improve the performance of the MPPT, application of perturbation-based extremum seeking (PES) and model reference adaptive control (MRAC) is proposed. This control scheme can efficiently handle the uncertainties in the FC as well as the load, through two control levels. The first level is PES utilized to adjust the duty cycle of the DC-DC converter; and the second level is MRAC employed to achieve the desired dynamic response. Using the proposed control strategy, design and analysis of the control levels can be realized independently, which results in easy implementation. This is achieved due to considerable differences between the time constants of the control levels. The simulation results are utilized to confirm the effectiveness of the proposed scheme in response to the variations of FC parameters and load. Also, comparative studies with a combination of PES and PID controller are provided in the simulation.

In this paper, unstructured system identification algorithm based on orthonormal Laguerre functions is combined with predictive functional control such that similar classical PI controller is constructed. Lack of mathematical model and initial information about process is not a restriction for mentioned algorithm and unstructured system identification based on Laguerre functions can overcome these restrictions. Augmenting new state variables to system state space, a new algorithm is constructed. This algorithm has similar structure with classical PI controller and in predictive control's cost function, in addition to tracking error, system states is utilized, that leads to improve controller dynamical performance. This new algorithm is simulated on the superheated steam temperature system in thermal power plant. Simulation results show capabilities of this algorithm.

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For a second-order mechanical system incorporating Coulomb frictional effect, a nonlinear adaptive control that achieves a controller-identifier separation is designed. This modularity is made possible by the strong input-to-state stability (ISS) property of the ISS controller with respect to the parameter estimation error as input. This input is independently guaranteed to be bounded by the passive identifier. We use two types of passive identifiers: z-scheme passive identifier and x-scheme passive identifier. These designs are more flexible than the Lyapunov-based design and lead to lower control effort. In addition, the advantages and disadvantages of z-scheme and x-scheme are presented. Transient performance of the system is enhanced with a trajectory initialization technique. The validity and effectiveness of the proposed friction compensator is verified by simulation for position tracking control under the influence of Coulomb friction.

In this paper, Predictive Functional Control (PFC) is used for X-Y pedestal control for LEO satellite tracking. According to the nonlinear characteristics of the X-Y pedestal and pedestal model variation caused by its operating point change, the use of system identification algorithm, which is based on special types of orthonormal functions known as Laguerre functions, is presented. This algorithm is combined with PFC to obtain a novel adaptive control algorithm entitled Adaptive Predictive Functional Control (APFC). In this combination, Laguerre functions are utilized for system identification, while the PFC is the control law. An interesting feature of the proposed algorithm is its desirable performance against the interference effect of channel X and channel Y. The proposed APFC algorithm is compared with Proportional Integral Derivative (PID) controller using simulation results. The results confirm that the proposed controller improves the performance in terms of the pedestal model variations; that is, the controller is capable of adapting to the model changes desirably.

One of the main drawbacks of model reference adaptive control (MRAC) is the weakness of its transient performance. The key reason of this imperfection is parameter's estimation error convergence. For many cases in the closed-loop control, the plant input signal cannot satisfy the persistence of excitation (PE) condition which yields poor parameters estimation error convergence. In this paper, we use a fast perturbation-based extremum seeking (PES) scheme without steady-state oscillation as the parameter identifier in indirect MRAC. The estimated parameters through the PES identifier contain the additive sinusoidal signals with distinct frequencies in the transient, which satisfy the PE condition of the plant input. Therefore, convergence of the parameters estimation error to zero will be guaranteed that results in improvement of transient performance for indirect MRAC. Also, the contrary effects on the steady-state behaviour is eliminated since the sinusoidal excitation signals amplitude exponentially converge to zero and reinitiate with every change in the unknown parameters. Simulation results for a second order example have been presented to illustrate the effectiveness of the proposed scheme. KEYWORDS model reference adaptive control (MRAC), perturbation-based extremum seeking (PES) identifier, transient performance, indirect adaptive control